---
res:
  bibo_abstract:
  - 'We propose an algorithmic approach for synthesizing linear hybrid automata from
    time-series data. Unlike existing approaches, our approach provides a whole family
    of models with the same discrete structure but different dynamics. Each model
    in the family is guaranteed to capture the input data up to a precision error
    ε, in the following sense: For each time series, the model contains an execution
    that is ε-close to the data points. Our construction allows to effectively choose
    a model from this family with minimal precision error ε. We demonstrate the algorithm’s
    efficiency and its ability to find precise models in two case studies.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Miriam
      foaf_name: Garcia Soto, Miriam
      foaf_surname: Garcia Soto
      foaf_workInfoHomepage: http://www.librecat.org/personId=4B3207F6-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-2936-5719
  - foaf_Person:
      foaf_givenName: Thomas A
      foaf_name: Henzinger, Thomas A
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=40876CD8-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2985-7724
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Schilling, Christian
      foaf_surname: Schilling
      foaf_workInfoHomepage: http://www.librecat.org/personId=3A2F4DCE-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-3658-1065
  bibo_doi: 10.1007/978-3-031-19992-9_22
  bibo_volume: 13505
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:001456146500022
  dct_isPartOf:
  - http://id.crossref.org/issn/0302-9743
  - http://id.crossref.org/issn/1611-3349
  - http://id.crossref.org/issn/9783031199912
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Synthesis of parametric hybrid automata from time series@
...